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Measuring and monitoring collective attention during shocking events

He, Xingsheng and Lin, Yu-Ru (2017) Measuring and monitoring collective attention during shocking events.

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There has been growing interest in leveraging Web-based social and communication technologies for better crisis response. How might the Web platforms be used as an observatory to systematically understand the dynamics of the public’s attention during disaster events? And how could we monitor such attention in a cost-effective way? In this work, we propose an ‘attention shift network’ framework to systematically observe, measure, and analyze the dynamics of collective attention in response to real-world exogenous shocks such as disasters. Through tracing hashtags that appeared in Twitter users’ complete timeline around several violent terrorist attacks, we study the properties of network structures and reveal the temporal dynamics of the collective attention across multiple disasters. Further, to enable an efficient monitoring of the collective attention dynamics, we propose an effective stochastic sampling approach that accounts for the users’ hashtag adoption frequency, connectedness and diversity, as well as data variability. We conduct extensive experiments to show that the proposed sampling approach significantly outperforms several alternative methods in both retaining the network structures and preserving the information with a small set of sampling targets, suggesting the utility of the proposed method in various realistic settings.


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Item Type: Article
Status: Published
CreatorsEmailPitt UsernameORCID
He, Xingsheng
Lin, Yu-Ru
ContributionContributors NameEmailPitt UsernameORCID
CorrespondentLin, Yu-Ruyurulin@pitt.eduUNSPECIFIEDUNSPECIFIED
Date: November 2017
Volume: 6
Number: 30
DOI or Unique Handle: 10.1140/epjds/s13688-017-0126-4
Schools and Programs: School of Computing and Information > Information Science
Refereed: Yes
Uncontrolled Keywords: collective attentiondisaster responsecollective intelligenceattention sampling
Article Type: Research Article
Date Deposited: 05 Jul 2018 19:43
Last Modified: 05 Jul 2018 19:43

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